{"id":"W1986647192","doi":"10.1080/19439342.2011.626059","title":"How can sanitary infrastructures reduce child malnutrition and health inequalities? Evidence from Guatemala","year":2011,"lang":"en","type":"article","venue":"Journal of Development Effectiveness","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Hôpital Fleurimont","funders":"","keywords":"Malnutrition; Poverty; Economic growth; Environmental health; Inequality; Business; Promotion (chess); Propensity score matching; Sanitation; Public health; Child mortality; Impact evaluation; Economics; Development economics; Socioeconomics; Developing country; Medicine; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005473319,0.0005759951,0.0009296046,0.001605249,0.0005862058,0.0009456646,0.0009220361,0.0007820376,0.004064011],"category_scores_gemma":[0.01857407,0.0002696308,0.001771842,0.003722301,0.001493235,0.0005569081,0.001931782,0.0005559516,0.0002009564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001903957,"about_ca_system_score_gemma":0.002428912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08730146,"about_ca_topic_score_gemma":0.1220833,"domain_scores_codex":[0.9948579,0.003760622,0.0002621368,0.0003045487,0.0002669208,0.0005478344],"domain_scores_gemma":[0.9906329,0.005209859,0.002744185,0.0006543166,0.0004123505,0.000346307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005352597,0.0005649316,0.7833182,0.01289296,0.01440645,0.001158103,0.003822826,0.001801196,0.0006805196,0.00567866,0.003951438,0.1663721],"study_design_scores_gemma":[0.0007745409,0.001192283,0.9655258,0.003996794,0.01446754,0.0002072169,0.001630081,0.0007663627,0.0003292759,0.00162133,0.009453136,0.00003566648],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9029422,0.0744766,0.001178281,0.01039162,0.0001067173,0.0002189293,0.001888601,0.00003177124,0.008765333],"genre_scores_gemma":[0.9885347,0.01005818,0.0003338379,0.0005782304,0.00003140291,0.00006922876,0.0002472773,0.00000337653,0.0001437829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08730146,"threshold_uncertainty_score":0.1735866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05389807918942918,"score_gpt":0.29603541182534,"score_spread":0.2421373326359109,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}